HW5 - Make Data Move
Alexandra Pasternakova
05/10/2020
Explore global development with R
Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1## Warning: package 'purrr' was built under R version 3.5.3## Warning: package 'stringr' was built under R version 3.5.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Warning: package 'gapminder' was built under R version 3.5.3##
## Attaching package: 'scales'## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factorLook at the data and tackle the tasks
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...unique(gapminder$year)## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007head(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
- Why does it make sense to have a log10 scale on x axis?
-a log scale makes exponential growth appear linear on the graph, therefore, makes it asier to read it
- Who is the outlier (the richest country in 1952 - far right on x axis)?
gdpPercap_1952 <- gapminder %>%
filter(year == 1952) %>%
select(country, gdpPercap)
gdpPercap_1952[order(gdpPercap_1952$gdpPercap,decreasing=TRUE),]## # A tibble: 142 x 2
## country gdpPercap
## <fct> <dbl>
## 1 Kuwait 108382.
## 2 Switzerland 14734.
## 3 United States 13990.
## 4 Canada 11367.
## 5 New Zealand 10557.
## 6 Norway 10095.
## 7 Australia 10040.
## 8 United Kingdom 9980.
## 9 Bahrain 9867.
## 10 Denmark 9692.
## # ... with 132 more rows#filtering out everything except the year we want and then selecting collumns country and gdp
#below is a tibble of countries ordered by the gdpPercap value in year 1952- the richest country is Kuwait
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
- Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) +
#I added colour mapping for continets
geom_point() +
scale_x_log10(labels = label_number())+
scale_size_continuous(labels = label_number())+
#and removed the scientific notations
labs(y= "Life expenctancy", x = "GDP per capita")
#changes x and y axis names to be more clear- What are the five richest countries in the world in 2007?
gdpPercap_2007 <- gapminder %>%
filter(year == 2007) %>%
select(country, gdpPercap)
#filtering out everything except the year we want and then selecting collumns country and gdp
gdpPercap_2007 %>%
arrange(desc(gdpPercap)) %>%
slice(1:5) ## # A tibble: 5 x 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.#arranging by value then showing 5 countries with highest values of gdpPercapMake it move!
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
Option 1: Animate using transition_states()
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
Option 2 Animate using transition_time()
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages
- Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for
transition_states()andtransition_time()functions respectively)
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point() +
labs(title = "Year: {as.integer(frame_time)}",
#frame_time gives the time that the current frame corresponds to
x = "GDP per capita",
y = "Life expectancy",
#changed names of x and y axis
size = "Population",
colour = "Continent") +
scale_x_log10(labels = label_number())+
scale_size_continuous(labels = label_number())+
transition_time(year)
#intended for data where the states are representing specific point in time
anim2
- Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.
-see above code in question 5, it already includes it all
anim2
- Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the
gapminder_unfiltereddataset and download more at https://www.gapminder.org/data/ ]
#I disgracefully borrowed the idea from the brackets and compared mean life expectancy on different continents in years I and my mom were born.
mean_lifeExp_71_98 <- gapminder_unfiltered %>% select(continent,year, lifeExp) %>%
group_by(continent,year) %>% summarise(mean_life_exp = mean(lifeExp)) %>%
filter(year==1998|year==1971) ## `summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.#we'll only use information about continent, year and life expectancy, further only the years 1998 and 1971
#creates collumn with mean life expectancy
mean_lifeExp_71_98 <- mean_lifeExp_71_98 %>% mutate(year_character = as.character(year))
#converting year to characters
ggplot(mean_lifeExp_71_98, aes(x = year_character, y = mean_life_exp, fill = continent)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Mean life expectancy in years 1971 and 1998 ",
x = "Year",
y = "Mean life expectancy (years)",
fill = "Continent",
label = mean_lifeExp_71_98$mean_life_exp) 